diff --git a/modules/icing/pirep_goes.py b/modules/icing/pirep_goes.py
index e0f305a2177a8bf236d968f8ecdc87dfaacdb598..66b165d793f5ef867afd19aecec145bff36a082d 100644
--- a/modules/icing/pirep_goes.py
+++ b/modules/icing/pirep_goes.py
@@ -1443,7 +1443,7 @@ def run_mean_std(check_cloudy=False):
         print(dname,': (', mean, mean_i, mean_ni, ') (', std, std_i, std_ni, ') ratio: ', no_icing_to_icing_ratio)
         print(dname,': (', lo, lo_i, lo_ni, ') (', hi, hi_i, hi_ni, ') ratio: ', no_icing_to_icing_ratio)
 
-        mean_std_dct[dname] = (mean_ni, std_ni, lo_ni, hi_ni)
+        mean_std_dct[dname] = (mean, std, lo, hi)
 
     [h5f.close() for h5f in ice_h5f_lst]
     [h5f.close() for h5f in no_ice_h5f_lst]
@@ -1457,7 +1457,7 @@ def run_mean_std(check_cloudy=False):
 
 def run_mean_std_2(check_cloudy=False, no_icing_to_icing_ratio=5, params=train_params_day):
     params = ['cld_height_acha', 'cld_geo_thick', 'supercooled_cloud_fraction', 'cld_temp_acha', 'cld_press_acha',
-            'cld_reff_dcomp', 'cld_opd_dcomp', 'cld_cwp_dcomp', 'iwc_dcomp', 'lwc_dcomp']
+              'cld_reff_dcomp', 'cld_opd_dcomp', 'cld_cwp_dcomp', 'iwc_dcomp', 'lwc_dcomp']
 
     mean_std_dct = {}
 
@@ -1539,30 +1539,6 @@ def run_mean_std_3(train_file_path, check_cloudy=False, params=train_params_day)
     pickle.dump(mean_std_lo_hi_dct, f)
     f.close()
 
-# def split_data(num_obs, perc=0.2, skip=1, shuffle=True, seed=None):
-#     idxs = np.arange(num_obs)
-#     idxs = list(idxs)
-#
-#     num_test = int(num_obs * perc)
-#
-#     test_idxs = idxs[::int(num_obs / num_test)]
-#
-#     test_set = set(test_idxs)
-#     train_set = (set(idxs)).difference(test_set)
-#     train_idxs = list(train_set)
-#
-#     test_idxs = np.array(test_idxs)
-#     train_idxs = np.array(train_idxs)
-#
-#     if seed is not None:
-#         np.random.seed(seed)
-#
-#     if shuffle:
-#         np.random.shuffle(test_idxs)
-#         np.random.shuffle(train_idxs)
-#
-#     return train_idxs[::skip], test_idxs[::skip]
-
 
 def split_data(times):
     time_idxs = np.arange(times.shape[0])